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Quantitative analysis of financial
markets has a long and distinguished
history that reaches back to the early
20th century with the publication of
the groundbreaking paper "The Theory
of Speculation" by Louis Bachelier
in 1900. It was the first scientifically
thorough study of statistical behaviour
of stock prices. Unfortunately, his
work did not receive the due attention
it deserved. Bachelier was far ahead
of his times. To a certain extent his
misfortune was that his ideas lacked
a practical catalyst: modern information
technology. Ultimately the progress
of modern finance and quantitative investing,
in particular, has always been tied
to the rapid advancement of computing
power and development of comprehensive
financial databases.

Finance took another leap forward in
the early 1950s when Harry Markowitz
introduced Modern Portfolio Theory (MPT),
which auspiciously fell into the same
timeframe as the invention of the first
modern computer. Subsequently, William
Sharpe extended MPT to the now famous
Capital Asset Pricing Model (CAPM),
upon which Fama & French later built
their three-factor CAPM model, taking
into account not only the overall market
but also size (small versus large caps)
and valuation (high P/B versus low P/B)
as explanatory factors for stock returns.

To the early quant investors these
market models provided the foundation
for their search for alpha. But instead
of looking at factors to explain historical
returns they turned the models around
using factors to forecast stock alpha.
The classic multi-factor quant strategy
was born.

Quant Investing-Where are we today?

Quant investing nowadays comes in many
shapes and forms. Often it is still
being misunderstood as the infamous
"black box". The following
provides a brief description of the
most common quantitative strategies
and talks about some of the important
points to look out for when evaluating
a quant investment process. Investors
armed with the right knowledge will
find that quant investing is anything
but a "black box".

The classic multi-factor model is an
example of a fundamentally driven quant
strategy. The way it works is very similar
to how a traditional stock analyst operates.
In both approaches the basis of an investment
decision is driven by a company's fundamental
data (balance sheet, income statement,
cash flow statement, financial ratios).
The difference is that a stock analyst
can deepen his analysis by interviewing
management, talking to competitors or
visiting production facilities. A quant
model is limited to rating stocks based
on the strength of numbers alone (EPS
growth, P/E ratio, profit margin, etc).
It can get some additional depth by
tracking analysts' estimates, dispersion
of estimates and rating changes. However,
even though a quant model may not match
an analyst in terms of depth of analysis,
it excels in breadth. Thanks to today's
abundance of computing power and extensive
financial databases a quant model can
monitor and analyse many more stocks
in a systematic fashion than any human
analyst ever could. Therefore a quant
model's main strength lies in the disciplined
process of applying a small edge repeatedly
to find as many independent investment
opportunities as possible. The resulting
broad diversification increases the
chances of achieving superior risk-adjusted
returns.

The same principle applies to all quant
strategies. Another important class
are the purely statistically driven
models which look at statistical properties
of price action between two or more
securities and derivatives. The standard
example is pairs trading. Let's look
at two companies that are related by
being operational in the same business,
say, two Japanese insurance companies.
In the absence of major company-specific
events it is quite likely that these
two companies will trade in line and
in the short term one company should
not outperform the other significantly.
Any such deviation should revert back
to the mean. Statistics gives a quant
manager the right tools to analyse whether
two companies indeed trade in line and
whether the odds for a profitable mean-reversion
trade are favourable. This strategy
is particularly computing power dependent.
For example, let's look at all constituents
of the Topix 500. We can form potentially
124,750 pairs. Even if only 1% of these
pairs pass tests for mean reversion
we still have to monitor more than 1,000
trades at any given time. Again, a small
edge (reversion to the mean) is applied
many times over (all combinations of
suitable pairs) to yield superior risk-adjusted
returns.

Lastly, let's examine another big branch
of quant strategies: technically driven
models. CTAs or systematic trading funds
are the best examples in this category.
The basic premise of these funds is
that everything you need to know about
a security (in this case mostly futures
contracts) is in its price and volume
traded. Usually several different models
are being used, eg, market timing models
for entry and exits, pattern recognition
and momentum systems for trend analysis
or mean reverting technicals for counter-trend
trades. Any of these methods applied
in isolation to only one security is
not going to show impressive results.
But just as before, applying all the
various models on all the different
futures contracts (typically we are
talking about more than 50 futures markets)
brings about the same diversification
effect that allows a small edge to become
a significant risk-adjusted return.

Strengths of quant investing

Widest possible
investment universe. Computers
do not mind crunching numbers, so
anything that can be quantified and
stored in a database can also be analysed
as a potential investment input. This
way quant investing can easily take
advantage of opportunities in smaller
under-researched companies and cover
more companies in general.

Diversification.
Every quant model in one way or another
tries to exploit a small sustainable
edge over many different (independent)
trades. It can be shown mathematically
that this will lead to a direct improvement
in risk-adjusted returns (see Law
of Active Management, Grinold &
Kahn).

Disciplined
process. Models are unemotional
in their decision making process.
Unlike human investors at times, models
don't get "married" to an
investment.

Weaknesses of quant investing

Depth of
analysis is limited. Traditional
analysts have an advantage in sourcing
additional non-quantifiable information,
eg, assessment of management quality,
corporate actions and their impact,
industry gossip, hands-on research
as in factory visits, etc.

Strong dependence
on data. Clearly a high quality
financial database is necessary to
run a credible quant research effort.
Quant managers and investors alike
should be aware of database limitations.
For example, extreme data points:
Quite often these points are entry
errors of some sort. However, they
could be valid data. Or backfilling
of data: Toyota Motor Corp reported
EPS for financial year 04/05 on 10
May 05. Many databases however will
record the EPS number as of 31 March
05, the official financial year-end
date.

Data mining
and over-optimisation. If
something looks too good to be true
it often is. A model that may be fine
tuned to deliver staggering results
in a historical back-test is very
likely to disappoint going forward.
Look for a common sense approach in
terms of portfolio composition.

Quant Investing - Performance Outlook

The sheer variety of quant strategies
makes it impossible to give a one-fits-all
answer but there are a few scenarios
one can look out for that may threaten
performance.

Fundamentally driven
models work on the assumption that
markets reward sensible investment
decisions in the long run, eg, a cheap
and profitable company should do better
than an expensive and loss-making
one given a reasonable time frame.
The problem is that in a "junk
rally" like the Internet bubble
the exact opposite is the case. In
this situation a fundamental model
will go through a tough patch. Investors
may want to check for components of
the model that complement the pure
fundamental approach, eg, market technicals
like momentum.

Statistically driven
models are sensitive event risks.
Most stats models try to exploit small
pricing inefficiencies. Hence, price
gaps caused by corporate events or
other unexpected news may cause big
losses relative to the expected profit
of a trade. Investors should look
out for features like position limits,
stop-loss limits and broad diversification.

CTAs / Systematic
Trading Funds are broadly speaking
trend-followers. Lack of clear macro
themes will likely result in "whipsawing".
Investors should find out to what
extent the model can cope with non-trending
markets.

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